This document discusses a privacy protection scheme for social network data that allows individual users to select which features of their profiles they want to conceal. The social networks are modeled as graphs where users are nodes and features are labels that can be marked as sensitive or non-sensitive. Algorithms are presented that transform the original graph into a new graph where nodes are sufficiently indistinguishable so that an adversary cannot infer a node's identity or sensitive labels from its neighborhood. The goal is to preserve as much of the original graph structure, properties, and non-sensitive information as possible while providing stronger privacy guarantees than previous methods.

1. Sensitive Label Privacy Protection on Social
Network Data
ABSTRACT
This project is motivated by the recognition of the need for a finer grain and more personalized
privacy in data publication of social networks. We propose a privacy protection scheme that not
only prevents the disclosure of identity of users but also the disclosure of selected features in
users' profiles. An individual user can select which features of her profiles she wishes to
conceal. The social networks are modeled as graphs in which users are nodes and features are
labels. Labels are denoted either as sensitive or as non-sensitive. We treat node labels both as
background knowledge an adversary may possess, and as sensitive information that has to be
protected. We present privacy protection algorithms that allow for graph data to be published in
a form such that an adversary who possesses information about a node's neighborhood cannot
safely infer its identity and its sensitive labels. To this aim, the algorithms transform the original
graph into a graph in which nodes are sufficiently indistinguishable. The algorithms are
designed to do so while losing as little information and while preserving as much utility as
possible. We evaluate empirically the extent to which the algorithms preserve the original
graph's structure and properties. We show that our solution is effective, efficient and scalable
while offering stronger privacy guarantees than those in previous research.
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2. Existing System
The current trend in the Social Network it not giving the privacy about user profile views. The
method of data sharing or (Posting) has taking more time and not under the certain condition of
displaying sensitive and non-sensitive data.
Problems on existing system:
1. There is no way to publish the Non sensitive data to all in social Network.
2. It’s not providing privacy about user profiles.
3. Some mechanisms that prevent both inadvertent private information leakage and attacks
by malicious adversaries.
Proposed System
Here, we extend the existing definitions of modules and we introduced the sensitive or non-
sensitive label concept in our project. We overcome the existing system disadvantages in our
project.
Advantages:
1. We can publish the Non sensitive data to every-one in social Network.
2. It’s providing privacy for the user profiles so that unwanted persons not able to view
your profiles.
3. We can post sensitive data to particular peoples and same way we can post non-
sensitive data to everyone like ads or job posts.
IMPLEMENTATION

3. Implementation is the stage of the project when the theoretical design is turned out into a
working system. Thus it can be considered to be the most critical stage in achieving a
successful new system and in giving the user, confidence that the new system will work and
be effective.
The implementation stage involves careful planning, investigation of the existing
system and it’s constraints on implementation, designing of methods to achieve changeover
and evaluation of changeover methods.
Main Modules:-
1. User Module:
In this module, Users are having authentication and security to access the detail
which is presented in the ontology system. Before accessing or searching the details user
should have the account in that otherwise they should register first.
2.Information Loss:
We aim to keep information loss low. In- formation loss in this case contains both
structure information loss and label information loss. There are some non sensitive data’s are
Loss due to Privacy making so we can’t send out full information to the public.
3. Sensitive Label Privacy Protection:
There are who post the image to the online social network if allow the people for
showing the image it will display to his requesters it make as the sensitive to that user. Thsis is
very useful to make sensitive data for the public .

4. System Configuration:-
H/W System Configuration:-
Processor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
S/W System Configuration:-
 Operating System :Windows95/98/2000/XP
 Application Server : Tomcat5.0/6.X
 Front End : HTML, Java, Jsp
 Scripts : JavaScript.
 Server side Script : Java Server Pages.
 Database : Mysql 5.0
 Database Connectivity : JDBC.